metadata
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
base_model: joeddav/xlm-roberta-large-xnli
model-index:
- name: xlm-roberta-large-xnli-finetuned-mnli
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
args: mnli
metrics:
- type: accuracy
value: 0.8548888888888889
name: Accuracy
xlm-roberta-large-xnli-finetuned-mnli
This model is a fine-tuned version of joeddav/xlm-roberta-large-xnli on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 1.2542
- Accuracy: 0.8549
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7468 | 1.0 | 2250 | 0.8551 | 0.8348 |
| 0.567 | 2.0 | 4500 | 0.8935 | 0.8377 |
| 0.318 | 3.0 | 6750 | 0.9892 | 0.8492 |
| 0.1146 | 4.0 | 9000 | 1.2373 | 0.8446 |
| 0.0383 | 5.0 | 11250 | 1.2542 | 0.8549 |
Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1